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BIOMEDICAL IMAGING GROUP (BIG)
Laboratoire d'imagerie biomédicale (LIB)
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Seminar 00083.txt

Regularized Interpolation for Noisy Data
Sathish Ramani, BIG

Test Run • 03 April 2007 • BM 4.235

Abstract
Interpolation is a vital tool in biomedical signal processing. Although there exists a substantial literature dedicated to noise-free conditions, much less is known in the presence of noise. Here, we document the breakdown of standard interpolation for noisy data and study the performance improvement due to regularized interpolation. In particular, we numerically investigate the Tikhonov (quadratic) regularization. On top of that, we explore non-quadratic regularization and show that this yields further improvements. We derive a novel bounded regularization approach to determine the optimal solution. We justify our claims with experimental results.
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